Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections – 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: claims 1- 20 are directed to either a process, machine, manufacture or composition of matter. With respect to claim s 1 , 20 : 2A Prong 1 : selecting, [ with the computer system ] , a plurality of subsets of the first set of training data, a first subset among the plurality of subsets representing a first interval of time and a second subset among the plurality of subsets representing a second interval of time after the first interval of time (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data); optimizing parameters of the first [ machine-learning model ] with a first objective function based on an accuracy of the first machine-learning model ( reads as math concept or retraining, see below; mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation); predicting attributes of the second subset based on the attributes of the first subset (using AI to predict amounts to a mental process in the same way that a human can predict the weather with or without a computer); generating, [ with the computer system ] , a virtual set of training data, comprising: virtual events in a third interval after the first interval; (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data); and virtual events in a fourth interval after the third interval; [training, with the computer system], a second [machine-learning model] on the virtual set of training data by optimizing parameters of the [ second machine-learning model ] with a second objective function based on an accuracy of the second machine-learning model in [ predicting attributes ] of the fourth subset based on the attributes of the third subset (further expand mental process also reads on modeling; mental process of modeling with assistance of pen and paper) . 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: Claim 1 A tangible, non-transitory, machine-readable medium storing instructions (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); Processor s , computer system ( computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358 ); obtaining, with a [ computer system ] , a first set of training data, the first set of training data comprising a time-series of events that are caused by an actor entity, and at least some events of the time-series of events comprising a plurality of attributes (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)) ; training, with the computer system, a first [ machine-learning model ] on the first set of training data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data ); machine learning model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)); optimizing parameters can also read on retraining/iterative training (the court finds that this training is generic and summarily states the process of the training a model is required for any e.g., machine learning model. Using a machine learning technique necessarily includes an iterative step training step; iterative training using selected training material and/or dynamic adjustments based on changes are incident to the very nature of machine learning); using the trained machine learning model to predict… (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction ); storing, with the computer system, the trained second machine-learning model in memory ( mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)). 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: Claim 1 A tangible, non-transitory, machine-readable medium storing instructions (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)); Processor s, computer system ( computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358 ); obtaining, with a [ computer system ] , a first set of training data, the first set of training data comprising a time-series of events that are caused by an actor entity, and at least some events of the time-series of events comprising a plurality of attributes (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)) ; training, with the computer system, a first [ machine-learning model ] on the first set of training data (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data ); machine learning model (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)); optimizing parameters can also read on retraining/iterative training (the court finds that this training is generic and summarily states the process of the training a model is required for any e.g., machine learning model. Using a machine learning technique necessarily includes an iterative step training step; iterative training using selected training material and/or dynamic adjustments based on changes are incident to the very nature of machine learning); using the trained machine learning model to predict… (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level application of a previously trained model to make a prediction ); storing, with the computer system, the trained second machine-learning model in memory ( mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)) ; Further, the receiving/ storing steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE , Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer . The claim is not patent eligible. 2. The medium of claim 1, wherein: the first set of training data comprise a plurality of classifications; and the first machine-learning model is trained based on the plurality of classifications (further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 3. The medium of claim 1, wherein the generation of the virtual set of training data comprises repeatedly adding new time-series of events that are caused by the actor entity ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 4. The medium of claim 1, wherein: the third subset comprises exogenous events ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 5. The medium of claim 1, the operations further comprising: filtering attributes from the second subset that are identical to the attributes of the first subset (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data ; can also read as a math concept to accomplish the filtering ) . 6. The medium of claim 1, wherein: the third subset comprises events that are caused by other actor entities, wherein the events caused by other actor entities occurred before the third interval ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 7. The medium of claim 1, wherein: obtaining the first set of training data further comprises filtering the events that occurred before a designated date from the time-series of events ( encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data ; can also read as a math concept to accomplish the filtering ) . 8. The medium of claim 1, wherein: at least a portion of the second interval overlaps with at least a portion of the third interval ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 9. The medium of claim 1, wherein: the attributes of at least some events comprise a question and a response received from the actor entity ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 10. The medium of claim 1, wherein: at least some events comprise offers presented to the actor entity and offers accepted by the actor entity ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 11. The medium of claim 1, the operations further comprising: adjusting parameters of a value function indicative of an occurrence probability of an attribute in the fourth subset ( encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data ; can also read as a math concept to accomplish the probability calculations ) . 12. The medium of claim 11, the operations further comprising: scoring attributes of the fourth subset based on the occurrence probability calculated by the value function ( encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data ; can also read as a math concept to accomplish the scoring and probability calculations ) . 13. The medium of claim 12, the operations further comprising: generating sequences of future events based on the score ( math concept to accomplish the scoring ) of the attributes of the fourth subset ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 14. The medium of claim 1, wherein: the first machine learning model is part of a value function ( math concept to accomplish the value function ) or an environment model of a reinforcement learning model (additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)) ; and the second trained machine learning model is a random decision forest model that includes a plurality of weighted trained decision trees (additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)) . 15. The medium of claim 1, wherein: the first plurality of subsets comprises more than 100,000 different attributes; the first or the second machine learning model executes on a compute cluster ( math concept to accomplish the value function ) having a plurality of computing devices that collectively perform an in-memory cluster computing ( math concept to accomplish the value function ) ; program state upon which the first or the second machine learning model operates is stored in an in-memory, immutable, distributed dataset spread over a plurality of nodes of the compute cluster such that the distributed dataset is resilient to failure of a given one of the computing devices; and the compute cluster (compute cluster also reads as an additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)) concurrently processes data in the distributed dataset to apply or train the first or the second machine learning model. 16. The medium of claim 1, wherein: training the first machine-learning model comprises steps for training a supervised time- series forecasting model; and training the second machine- learning model comprises steps for training a supervised classification model ( additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)) . 17. The medium of claim 1, wherein: the operations comprise steps for causing at least some of the attributes of the fourth plurality of the subsets to respond to the actor entity in a targeted manner based on a trained model (further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 18. The medium of claim 1, wherein: the operations comprise steps for predicting probability of the attributes of the fourth plurality of the subsets related to the actor entity (further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . 19. The medium of claim 1, the operations further comprising: adjusting, through a plurality of iterations, parameters of the second machine-learning model to increase the accuracy of the second machine-learning model in predicting the attributes of the fourth plurality of the subsets ( further expand mental process; t he claims amount to data analysis/manipulation and using some form of AI as a tool) . Double Patenting A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co. , 151 U.S. 186 (1894); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert , 245 F.2d 467, 114 USPQ 330 (CCPA 1957). A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101. Claims 1-20 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 5-24 of prior U.S. Patent No. 11,537,878 . This is a statutory double patenting rejection. Claims are duplicates. 17/987737 11,537,878 1 5 2 6 3 7 4 8 5 9 6 10 7 11 8 12 9 13 10 14 11 15 12 16 13 17 14 18 15 19 16 20 17 21 18 22 19 23 20 24 Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.— The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1 and 20 recite the limitation "the fourth subset and the third subset". There is insufficient antecedent basis for these limitations in the claims. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sachs (US 2019/0294975) teaches machine learning component is used to predict behavior of the physical entity in a hypothetical situation (0042). Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT DAVID R VINCENT whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-3080 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT ~Mon-Fri 12-8:30 . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Alexey Shmatov can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 5712703428 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DAVID R VINCENT/ Primary Examiner, Art Unit 2123